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In computer vision, the bag-of-visual words(BOV) approach has been shown to yield state-of-the-art results. To improve BOV model, we use sparse codes of SIFT features instead of previous vector quantization (VQ) such as k-means, due to more quantization errors of VQ. And as local features in most categories have spatial dependence in real world, we use neighbor features of one local feature as its...
The bag-of-words approach is gained much research in object categorization. Creating visual codebook is an important problem in object categorization. The non-informative codeword will increase the vocabulary size which brings more computation cost, and cannot improve the classification performance. We first define a weighted minimal-redundancy-maximal-relevance criterion (mRMR) which is an extension...
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